# Scientific software

## SymPy

This is the dream cheating software every student should know about.
It also has serious applications obviously. www.sympy.org/scipy-2017-codegen-tutorial/ mentions code generation capabilities, which sounds super cool!
The code in this section was tested on sympy==1.8 and Python 3.9.5.
from sympy import *
sympify(2)/3 + sympify(1)/2
outputs:
7/6
Note that this is an exact value, it does not get converted to floating-point numbers where precision could be lost!
We can also do everything with symbols:
from sympy import *
x, y = symbols('x y')
expr = x/3 + y/2
print(expr)
outputs:
x/3 + y/2
We can now evaluate that expression object at any time:
expr.subs({x: 1, y: 2})
outputs:
4/3
x = sqrt(2)
print(x)
outputs:
sqrt(2)
so we understand that the value was kept without simplification. And of course:
sqrt(2)**2
outputs 2. Also:
sqrt(-1)
outputs:
I
I is the imaginary unit. We can use that symbol directly as well, e.g.:
I*I
gives:
-1
Let's do some trigonometry:
cos(pi)
gives:
-1
and:
cos(pi/4)
gives:
sqrt(2)/2
The exponential also works:
exp(I*pi)
gives;
-1
Now for some calculus. To find the derivative of the natural logarithm:
from sympy import *
x = symbols('x')
diff(ln(x), x)
outputs:
1/x
Just read that. One over x. Beauty.
Let's do some more. Let's solve a simple differential equation:
y''(t) - 2y'(t) + y(t) = sin(t)
Doing:
from sympy import *
x = symbols('x')
f, g = symbols('f g', cls=Function)
diffeq = Eq(f(x).diff(x, x) - 2*f(x).diff(x) + f(x), sin(x)**4)
print(dsolve(diffeq, f(x)))
outputs:
Eq(f(x), (C1 + C2*x)*exp(x) + cos(x)/2)
which means:
To be fair though, it can't do anything crazy, it likely just goes over known patterns that it has solvers for, e.g. if we change it to:
diffeq = Eq(f(x).diff(x, x)**2 + f(x), 0)
it just blows up:
NotImplementedError: solve: Cannot solve f(x) + Derivative(f(x), (x, 2))**2
Let's try some polynomial equations:
from sympy import *
x, a, b, c = symbols('x a b c d e f')
eq = Eq(a*x**2 + b*x + c, 0)
sol = solveset(eq, x)
print(sol)
which outputs:
FiniteSet(-b/(2*a) - sqrt(-4*a*c + b**2)/(2*a), -b/(2*a) + sqrt(-4*a*c + b**2)/(2*a))
which is a not amazingly nice version of the quadratic formula. Let's evaluate with some specific constants after the fact:
sol.subs({a: 1, b: 2, c: 3})
which outputs
FiniteSet(-1 + sqrt(2)*I, -1 - sqrt(2)*I)
Let's see if it handles the quartic equation:
x, a, b, c, d, e, f = symbols('x a b c d e f')
eq = Eq(e*x**4 + d*x**3 + c*x**2 + b*x + a, 0)
solveset(eq, x)
Something comes out. It takes up the entire terminal. Naughty. And now let's try to mess with it:
x, a, b, c, d, e, f = symbols('x a b c d e f')
eq = Eq(f*x**5 + e*x**4 + d*x**3 + c*x**2 + b*x + a, 0)
solveset(eq, x)
and this time it spits out something more magic:
ConditionSet(x, Eq(a + b*x + c*x**2 + d*x**3 + e*x**4 + f*x**5, 0), Complexes)
Oh well.
Let's try some linear algebra.
m = Matrix([[1, 2], [3, 4]])
Let's invert it:
m**-1
outputs:
Matrix([
[ -2,    1],
[3/2, -1/2]])

## Open source scientific computing consultancies

Huge respect to this companies.

## Matplotlib

It does a huge percentage of what you want easily, and from the language that you want to use.
Tends to be Ciro's pick if gnuplot can't handle the use case, or if the project is really really serious.
Tested on Python 3.10.4, Ubuntu 22.04.

## gnuplot

Tends to be Ciro Santilli's first attempt for quick and dirty graphing: github.com/cirosantilli/gnuplot-cheat.
domain-specific language. When it get the jobs done, it is in 3 lines and it feels great.
When it doesn't, you Google for an hours, and then you give up in frustration, and fall back to Matplotlib.

## gnuplot command line hello world

CLI hello world:
gnuplot -p -e 'p sin(x)'